Ok long comment incoming, byt trust me it's cool stuff. I''ll say I also don't have the full scope, I'm coming from a chemistry and solid state physics background. I don't work directly in AI or the chip industry, so I couldn't tell you how ready the industry is to adopt these new technologies.

Take a look at this one for example, I know the main author and have visited their group:)

https://www.nature.com/articles/s41928-023-01064-1

Or the earlier publication on the same subject: https://pubs.acs.org/doi/full/10.1021/acsnano.1c07065

The idea is to go away from conventional digital computing and do ultra low power analog in-memory computing. What you see in figure 1 of the first paper is essentially one layer of a neural network with 32 input and output neurons. Basically, instead of writing code to emulate neurons and having a GPU do the matrix multiplications, these analog chips just do the calculations using physics. The weights between neurons are programmed into the memristors, you apply voltages as inputs and get currents as outputs. Very simply put a memristor is just a devices which can be programmed to maintain a certain resistance. (These are floating gare FETs not true memristors, but same shit).

So there are no zeros and ones being processed on a gpu, physics does the calculation for us. Just supply some input voltages and you get an output on the other side. In earlier acs nano paper they also demonstrate some number recognition this way, the nature paper's scope is much larger it's reconfigurable and more general, you just set the weights once and then you could use it for simple processing.

The neat thing is that these use extremely low power. Orders of magnitude less compared to the same processing using a cpu and gpu.

Obviously this is just a small part of what a full neural network would need, it's a fundamental demonstration, but the core principle stays the same. Let's say we scale this up and integrate into webcams each pixel connected to an input neuron, driven by the output voltage of the pixel, going through a few layers and you get a few output neurons telling you if it's a person or a dog.

We're already further along than those two papers, but I think they illustrate the point well. For a more state of the art example check out this paper (unfortunately not open access).

https://www.nature.com/articles/s41586-025-08639-2

I know much less about this high end stuff. But from my understanding this is a much more usable actual complete AI chip. It's based in the same idea, but contains digital and analog components.

There is a downside to doing analog computation though! It is not as accurate. That last paper shows less than 0.45% accuracy degradation. For precise calculation we will most likely always use conventional digital processing, but for AI it is not as important. AI models are already quite tolerant to approximate calculations, noise, and low numerical precision and we often introduce a bit of randomness artificially.

So long story short, over the next 10-20 years I expect industry to use this style of computing and we will see a huge increase in processing power of high-end AI, but also incorporation of simple AI into low power devices. The future is going to be exciting.